activity
20182020
most citedMachine Learning Inter-Atomic Potentials Generation Driven by Active Learning: A Case Study for Amorphous and Liquid Hafnium dioxide

189 citations · 306 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2020★ 1 cited

Flame Stability Analysis of Flame Spray Pyrolysis by Artificial Intelligence

Jessica Pan, Joseph A. Libera, Noah H. Paulson +1

Flame spray pyrolysis (FSP) is a process used to synthesize nanoparticles through the combustion of an atomized precursor solution; this process has applications in catalysts, batt…

cs.CV2020★ 2 cited

Towards Online Steering of Flame Spray Pyrolysis Nanoparticle Synthesis

Maksim Levental, Ryan Chard, Joseph A. Libera +8

Flame Spray Pyrolysis (FSP) is a manufacturing technique to mass produce engineered nanoparticles for applications in catalysis, energy materials, composites, and more. FSP instrum…

physics.comp-ph2020

Uncertainty Quantification in Atomistic Modeling of Metals and its Effect on Mesoscale and Continuum Modeling A Review

Joshua J. Gabriel, Noah H. Paulson, Thien C. Duong +4

The design of next-generation alloys through the Integrated Computational Materials Engineering (ICME) approach relies on multi-scale computer simulations to provide thermodynamic…

cond-mat.mtrl-sci2020★ 55 cited

An Experimentally Driven Automated Machine Learned lnter-Atomic Potential for a Refractory Oxide

Ganesh Sivaraman, Leighanne Gallington, Anand Narayanan Krishnamoorthy +4

Understanding the structure and properties of refractory oxides are critical for high temperature applications. In this work, a combined experimental and simulation approach uses a…

cond-mat.mtrl-sci2019★ 189 cited

Machine Learning Inter-Atomic Potentials Generation Driven by Active Learning: A Case Study for Amorphous and Liquid Hafnium dioxide

Ganesh Sivaraman, Anand Narayanan Krishnamoorthy, Matthias Baur +5

We propose a novel active learning scheme for automatically sampling a minimum number of uncorrelated configurations for fitting the Gaussian Approximation Potential (GAP). Our act…

cond-mat.mtrl-sci2019★ 59 cited

Quantified Uncertainty in Thermodynamic Modeling for Materials Design

Noah H Paulson, Brandon J Bocklund, Richard A Otis +2

Phase fractions, compositions and energies of the stable phases as a function of macroscopic composition, temperature, and pressure (X-T-P) are the principle correlations needed fo…